Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Biodepth Data

Description

Data from the pan-European Biodepth grassland diversity manipulation

Author(s)

Jarrett Byrnes

References

Spehn, E. M., A. Hector, J. Joshi, M. Scherer-Lorenzen, B. Schmid, E. Bazeley-White, C. Beierkuhnlein, M. C. Caldeira, M. Diemer, P. G. Dimitrakopoulos, J. A. Finn, H. Freitas, P. S. Giller, J. Good, R. Harris, P. Hogberg, K. Huss-Danell, A. Jumpponen, J. Koricheva, P. W. Leadley, M. Loreau, A. Minns, C. P. H. Mulder, G. O'Donovan, S. J. Otway, C. Palmborg, J. S. Pereira, A. B. Pfisterer, A. Prinz, D. J. Read, E. D. Schulze, A. S. Siamantziouras, A. C. Terry, A. Y. Troumbis, F. I. Woodward, S. Yachi, and J. H. Lawton. 2005. Ecosystem effects of biodiversity manipulations in European grasslands. Ecological Monographs 75:37-63.

cor_dist

Description

Takes a data frame of functions and calculates the correlation-based distance between functions.

Usage

cor_dist(adf)

Arguments

Value

A matrix

divNeeded

Description

divNeeded Determines, for every combination of functions, how many species influence those functions.

Usage

divNeeded(overData, type = "positive")

Arguments

Details

Iterates over all possible combinations of functions. Checks the matrix of which species have positive, negative, or both influences on those functions. Tally's total number of species that have an effect on those functions

Value

Returns a data frame of all combinations and how many species are needed to influence all of them.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the
# same sign-scale (e.g. the maximum level of a
# function is the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

redund <- getRedundancy(vars, species, germany)

posCurve <- divNeeded(redund, type = "positive")

dmean

Description

Calculates the average distance between functions for one or an entire assemblage of replicates

Usage

dmean(adf_raw, D)

Arguments

Value

Single numeric of weighted average of distance matrix

References

Byrnes, J. E. K., Roger, F. and Bagchi, R. 2022. Understandable Multifunctionality Measures Using Hill Numbers. bioRxiv. 2022.03.17.484802. https://doi.org/10.1101/2022.03.17.484802

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

dmin

Description

Calculates the minimum non-zero value of a distance matrix

Usage

dmin(D)

Arguments

Value

A numeric

Seagrass Mesocosm Data

Description

Data from the a seagrass grazer diversity manipulation at the Virginia Institute of Marine Sciences. From Duffy et al. 2003 Ecology Letters.

Author(s)

Jarrett Byrnes

References

Duffy, J. E., J. P. Richardson, and E. A. Canuel. 2003. Grazer diversity effects on ecosystem functioning in seagrass beds. Ecology letters 6:637-645.

eff_num_func

Description

Calculate the effective number of functions for rows in a dataset

Usage

eff_num_func(dat, vars, q = 1, standardized = FALSE, D = NULL, tau = NULL)

Arguments

Details

Takes a data frame, variable names, whether we want an index standardized by number of functions or not, an order of Hill number for our effective number of functions as well as a dissimilarity matrix (if desired) and value for a dissimilarity cutoff (defaults to the average dissimilarity). It then calculates and returns the effective number of functions using the appropriate method. See Chao et al. 2019 for more.

Value

Returns a vector of effective or standardized effective number of functions

References

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.

Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.

eff_num_func_d

Description

eff_num_func_d

Usage

eff_num_func_d(adf_freq, q = 1, D, tau = NULL)

Arguments

Value

A vector of effective number of functions

References

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

eff_num_func_d_onerow

Description

eff_num_func_d_onerow

Usage

eff_num_func_d_onerow(arow_freq, D, tau, q)

Arguments

Value

A single value of effective number of functions

References

Byrnes, J. E. K., Roger, F. and Bagchi, R. 2022. Understandable Multifunctionality Measures Using Hill Numbers. bioRxiv. 2022.03.17.484802. https://doi.org/10.1101/2022.03.17.484802

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

eff_num_func_no_d

Description

eff_num_func_no_d

Usage

eff_num_func_no_d(adf_freq, q = 1)

Arguments

Details

Takes a data frame, with functions standardized against total level of function in their replicate as columns and replicates as rows. Returns the effective number of functions using the appropriate method. See Chao et al. 2019 or Jost 2006 for details. Does not adjust for correlation between functions.

Value

Returns a verctor of effective or standardized effective number of functions

References

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.

Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.

filterCoefData

Description

filterCoefData filters contributions of species to function by sign.

Usage

filterCoefData(coefData, type = "positive")

Arguments

Details

Takes a matrix of functions and coefficients for species and filters out only the sign of contributions desired. Typically used by other functions in the package.

Value

Returns a filtered matrix.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

coefs <- getRedundancy(vars, species, germany, output = "coef")
stdCoefs <- stdEffects(coefs, germany, vars, species)

filterCoefData(stdCoefs)


#########
# filterCoefData takes a matrix of coefficients
# and filters it so that only the positive, negative, or both contributions
# are present
#########

filterOverData

Description

filterOverData filters qualitative effects of species to function by sign.

Usage

filterOverData(overData, type = "positive")

Arguments

Details

Takes a matrix of functions and effects of species - 1's and -1's, s - and filters out only the sign of contributions desired. Typically used by other functions in the package.

Value

Returns a filtered matrix.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is the
# "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

redund <- getRedundancy(vars, species, germany)

filterOverData(redund, type = "positive")


#########
# filterOverData takes a matrix of 1s, 0s, and -1s
# and filters it so that only the positive, negative, or both contributions
# are 1 for later overlap function usage
#########

getCoefTab

Description

getCoefTab extract the effect of diversity on number of functions greater than a threshold

Usage

getCoefTab(
  eqn,
  fun = stats::glm,
  data,
  groupVar = "thresholds",
  coefVar = NULL,
  ...
)

Arguments

Details

getCoefTab Takes a statistical model and plot level data with the number of functions greater than a threshold at multiple different thresholds and returns the coefficient for the effect of diversity at each threshold

Value

Returns a data frame of thresholds, coefficients, and their statistical properties.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

germanyThresh <- getFuncsMaxed(germany, vars,
  threshmin = 0.05,
  threshmax = 0.99, prepend = c("plot", "Diversity"), maxN = 7
)

germanyLinearSlopes <- getCoefTab(funcMaxed ~ Diversity,
  data = germanyThresh, coefVar = "Diversity", family = quasipoisson(link = "identity")
)

getFuncMaxed

Description

getFuncMaxed the number of functions greater than or equal to a single threshold in one experimental unit

Usage

getFuncMaxed(
  adf,
  vars = NA,
  thresh = 0.7,
  proportion = FALSE,
  prepend = "Diversity",
  maxN = 1
)

Arguments

Details

Create a data frame that has the value of number or proportion of functions greater than a single threshold.

Value

Returns a data frame of number or fraction of functions greater than or equal to the selected thresholds in each plot.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

germanyThresh <- getFuncMaxed(germany, vars, 
                              thresh = 0.5, 
                              prepend = c("plot", "Diversity"), 
                              maxN = 7)


# A function that will return a data frame with the first several columns
# being information the user wants for identification purposes (prepend)
# which defaults to Diversity and the final column the number of columns
# which pass a predefined threshold, defined as some proportion of the maximim
# observed for each column.  vars=the names of the vars being specified
# thresh is the threshold, between 0 and 1, of proportion of the max that needs
# to be passed to be counted.

# changelog
# 2014-03-24 Fixed -1 error in getMaxValue
# 2015-06-24 Fixed column name from prepend error https://github.com/jebyrnes/multifunc/issues/1
# 2022-04-14 Updated to use dplyr

getFuncsMaxed

Description

getFuncsMaxed the number of functions greater than or equal to a wide variety of thresholds in each experimental unit

Usage

getFuncsMaxed(
  adf,
  vars = NA,
  threshmin = 0.05,
  threshmax = 0.99,
  threshstep = 0.01,
  proportion = FALSE,
  prepend = "Diversity",
  maxN = 1
)

Arguments

Details

Create a data frame that has the value of number or proportion of functions greater than a threshold for several different thresholds at the plot.

Value

Returns a data frame of number or fraction of functions greater than or equal to the selected thresholds in each plot over all thresholds within the relevant range.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + 
                  max(germany$N.Soil, na.rm = TRUE)

germanyThresh <- getFuncsMaxed(germany, vars,
  threshmin = 0.50,
  threshmax = 0.60, prepend = c("plot", "Diversity"), maxN = 7
)

getIndices

Description

getIndices Generates a variety of indices describing multifunctionality based on the number of functions greater than a threshold for many different threshold and coefficients describing the relationship between diversity and number of functoins greater than a threshold.

Usage

getIndices(
  slopedata,
  threshdata,
  eqn,
  fun = stats::glm,
  divvar = "Diversity",
  groupVar = "thresholds",
  showNfunc = TRUE
)

Arguments

Details

See Byrnes et al. In Review.

Value

A data frame of indices

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)

germanyThresh <- getFuncsMaxed(germany, vars,
  threshmin = 0.05,
  threshmax = 0.99, prepend = c("plot", "Diversity"), maxN = 7
)

germanyLinearSlopes <- getCoefTab(funcMaxed ~ Diversity,
  data = germanyThresh,
  coefVar = "Diversity", family = quasipoisson(link = "identity")
)

getIndices(germanyLinearSlopes, germanyThresh, funcMaxed ~ Diversity)

getMF_eff

Description

A multifunctionality index rooted in Hill numbers. getMF_eff get multifunctionality index defined by function and effective number of functions

Usage

getMF_eff(
  data,
  vars,
  q = 1,
  standardized = FALSE,
  standardize_function = standardizeUnitScale,
  D = NULL,
  tau = NULL
)

Arguments

Details

Takes a data frame, variable names, a standardizing function, whether we want an index standardized by number of functions or not, an order of Hill number for our effective number of functions as well as a dissimilarity matrix (if desired) and value for a dissimilarity cutoff (defaults to the average dissimilarity). It then calculates both the average standardized function in each plot and the effective number of functions and returns their product as a measure of effective multifunctionality.

Value

Returns a vector of effective or standardized effective multifunctionality.

Author(s)

Jarrett Byrnes.

References

Chao, A., Chiu, C.-H., Villéger, S., Sun, I.-F., Thorn, S., Lin, Y.-C., Chiang, J.-M. and Sherwin, W. B. 2019. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs. 89: e01343.

Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.

Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.

getOverlap

Description

getOverlap goes through all m-wise combinations of species and returns the amount of overlap between species in functions they perform for each combination

Usage

getOverlap(
  overData,
  m = 2,
  type = "positive",
  index = "sorensen",
  denom = "set"
)

Arguments

Details

getOverlap takes a matrix of 1s and -1s, and depending on whether we're interested in positive, negative, or both types of interactions looks for the m-wise overlap between species and returns the overlap index for each combination

Value

Returns a vector of overlap indices.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the
# same sign-scale (e.g. the maximum level of a function is the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

redund <- getRedundancy(vars, species, germany)

getOverlap(redund, m = 2)
getOverlap(redund, m = 2, index = "jaccard")
getOverlap(redund, m = 2, index = "mountford")

#########
# getOverlap takes a matrix of 1s and -1s, and depending on whether we're
# interested in positive, negative, or both types of interactions looks for the
# m-wise overlap
#########

getOverlapSummary

Description

getOverlapSummary summarizes the number of species necessary for each function including means, SDs, and other metrics

Usage

getOverlapSummary(
  overData,
  m = 2,
  type = "positive",
  index = "sorensen",
  denom = "set"
)

Arguments

Details

getOverlapSummary takes a matrix of 1s and -1s, and depending on whether we're interested in positive, negative, or both types of interactions looks for the m-wise overlap between species and then reports summary metrics of mean overlap, SD, and number of combinations

Value

Returns a data frame of the mean overlap, SD, and number of possible combinations.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

redund <- getRedundancy(vars, species, germany)

getOverlapSummary(redund, m = 2)


#########
# getOverlapSummary takes a matrix of 1s and -1s, and depending on whether we're
# interested in positive, negative, or both types of interactions looks for the
# m-wise overlap and then reports summary metrics of mean overlap, SD, and number of combinations
#########

getRedundancy

Description

getRedundancy examines which species have an effect on which function

Usage

getRedundancy(
  vars,
  species,
  data,
  negVars = NA,
  method = "lm",
  combine = "+",
  output = "effect",
  ...
)

Arguments

Details

getRedundancy takes a matrix of 1s,0s, and -1s, and depending on whether we're interested in positive, negative, or both types of interactions looks for the m-wise overlap between species and returns the overlap index for each combination. For species whose effect is not different from 0 at the alpha=0.05 level, a 0 is returned.

Value

Returns a matrix of functions and the effect of species on each. 1s, -1s, and 0s for "effect" or coefficients.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

getRedundancy(vars, species, germany)
getRedundancy(vars, species, germany, output = "coef")



#########
# takes a vector of responses, the species that may cause them
# and returns a table of 1s, -1s, and 0s with regards to the kind of effect
# or a coefficient table, if asked for.  Arugments can take the form of the fitting function
# how variables are combined, and additional arguments to the fitting function
#########

getStdAndMeanFunctions

Description

getStdAndMeanFunctions creates an average function multifunctionality index.

Usage

getStdAndMeanFunctions(data, vars, standardizeFunction = standardizeUnitScale)

Arguments

Details

iterates over all functions and standardizes them between 0 and 1. Then it creates an averaged multifunctionality index by averaging over all standardized functions

Value

Returns a data frame with standardized values for each function and an averaged index.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

germany <- cbind(germany, getStdAndMeanFunctions(germany, vars))

Quote Words

Description

qw Takes an unquoted vector and adds quotes to it like the qw function in perl.

Usage

qw(...)

Arguments

Details

This is a helper function for data processing. Honestly, I use qw all the time in other languages, and wanted a version for R.

Value

A vector

Author(s)

Jarrett Byrnes.

Examples

c("a", "b")

qw(a, b)



# qw - a helper function that we
# will use later to deal with strings
# analagous to qw in PERL

relevantSp

Description

relevantSp Which species are being used in this analysis.

Usage

relevantSp(data, colnums = 26:128)

Arguments

Details

Which columns have values that are greater than zero.

Value

A vector of columns names.

Author(s)

Jarrett Byrnes.

sAICfun

Description

sAICfun examines which species have an effect on which function using a stepwise AIC approach

Usage

sAICfun(
  response,
  species,
  data,
  positive.desired = TRUE,
  method = "lm",
  combine = "+",
  ...
)

Arguments

Details

sAICfun takes a dataset, response, and function, and then uses a stepAIC approach to determine the best model. From that it extracts the species with a positive, negative, and neutral effect on that function.

Value

Returns list of species with positive negative or neutral contributions, the relevant coefficient and effect matrices, and response name

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

spList <- sAICfun("biomassY3", species, germany)
# " spList
res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars



#########
# sAICfun takes a dataset, response, and function, and then uses a stepAIC approach
# to determine the best model.  From that it extracts the species with a positive,
# negative, and neutral effect on that function
#########

standardizeUnitScale

Description

standardizeUnitScale standardized a variable so its maximum is 1

Usage

standardizeUnitScale(afun, min0 = TRUE, maxValue = max(afun, na.rm = TRUE))

Arguments

Details

Takes a vector and then divides it by a maximum value.

Value

Returns a standardized vector.

Author(s)

Jarrett Byrnes.

standardizeZScore

Description

standardizeZScore Z-standardizes a vector.

Usage

standardizeZScore(afun)

Arguments

Details

Centers a vector and divides it by its standard deviation.

Value

Returns a z-standardized vector.

Author(s)

Jarrett Byrnes.

stdEffects

Description

stdEffects obtains the standardized effect of each species on each function

Usage

stdEffects(cmat, adf, vars, species)

Arguments

Details

stdEffects takes a matrix of coefficients for relationships between species and functions, the data frame used to generate those coefficients and the names of species and function, and then it calculates standardized coefficients using std coef = b *sx/sy

Value

Returns a matrix of standardized coefficients.

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))

# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)

res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars

coefs <- getRedundancy(vars, species, germany, output = "coef")
stdCoefs <- stdEffects(coefs, germany, vars, species)

#########
# A function that uses the coefficient matrix and information from the
# data to calculate standardized effects of species using the method
# std coef = b *sx/sy
#########

whichVars

Description

whichVars takes a data frame and the names of a set of columns and returns the names of those columns that do not have an excessive fraction of NA values

Usage

whichVars(a.df, vars = NA, thresh = 2/3)

Arguments

Details

This is a helper function for data processing.

Value

A vector of column names

Author(s)

Jarrett Byrnes.

Examples

data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)

germany <- subset(all_biodepth, all_biodepth$location == "Germany")

vars <- whichVars(germany, allVars)